Lorch B, Vaillant G, Baumgartner C, Bai W, Rueckert D, Maier A (2017)
Publication Language: English
Publication Status: Published
Publication Type: Journal article
Publication year: 2017
Book Volume: 2017
Article Number: 4501647
DOI: 10.1155/2017/4501647
Open Access Link: https://www.hindawi.com/journals/jme/2017/4501647/
The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.
APA:
Lorch, B., Vaillant, G., Baumgartner, C., Bai, W., Rueckert, D., & Maier, A. (2017). Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests. Journal of Medical Engineering, 2017. https://dx.doi.org/10.1155/2017/4501647
MLA:
Lorch, Benedikt, et al. "Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests." Journal of Medical Engineering 2017 (2017).
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